# How to integrate Zenserp MCP with CrewAI ```json { "title": "How to integrate Zenserp MCP with CrewAI", "toolkit": "Zenserp", "toolkit_slug": "zenserp", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/zenserp/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/zenserp/framework/crew-ai.md", "updated_at": "2026-05-06T08:34:37.106Z" } ``` ## Introduction This guide walks you through connecting Zenserp to CrewAI using the Composio tool router. By the end, you'll have a working Zenserp agent that can find top news articles on ai ethics, get trending keywords for electric cars, list local coffee shops in brooklyn through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Zenserp account through Composio's Zenserp MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Zenserp with - [OpenAI Agents SDK](https://composio.dev/toolkits/zenserp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zenserp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zenserp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zenserp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zenserp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zenserp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zenserp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zenserp/framework/cli) - [Google ADK](https://composio.dev/toolkits/zenserp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zenserp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zenserp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zenserp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zenserp/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Zenserp connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Zenserp - Build a conversational loop where your agent can execute Zenserp operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## What is the Zenserp MCP server, and what's possible with it? The Zenserp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zenserp account. It provides structured and secure access to real-time search engine results, so your agent can perform actions like running Google searches, grabbing news headlines, pulling images, analyzing trends, and even fetching local business data on your behalf. - Comprehensive Google and Bing search: Instantly run structured web searches and retrieve up-to-date SERP data from Google or Bing for any query. - Automated news and trend analysis: Have your agent fetch recent Google News articles or analyze keyword popularity over time using Google Trends data. - Reverse image and visual content search: Perform reverse image lookups or image searches to discover where an image appears online or find relevant pictures for any topic. - Shopping and video discovery: Search Google Shopping for product offers or Google Video for relevant multimedia results, all via agent-driven queries. - Local and map-based business lookup: Let your agent use Google Maps search to find businesses or places based on location, keywords, or coordinates for local intelligence. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ZENSERP_BING_SEARCH` | Bing Search | Tool to obtain bing search results. use when you need real-time scraping of bing serps from bing.com. | | `ZENSERP_GOOGLE_NEWS_SEARCH` | Google News Search | Tool to perform a google news search. use when you need recent news articles for a topic. example: "search news for climate change". | | `ZENSERP_GOOGLE_REVERSE_IMAGE_SEARCH` | Google Reverse Image Search | Tool to perform a reverse image search on google. use after obtaining a public image url to find where the image appears online. | | `ZENSERP_GOOGLE_SHOPPING_SEARCH` | Google Shopping Search | Tool to perform a google shopping search. use when you need structured product offers and pricing data via zenserp api. | | `ZENSERP_GOOGLE_TRENDS` | Google Trends | Tool to retrieve google trends data. use when comparing keyword popularity over time. | | `ZENSERP_GOOGLE_VIDEO_SEARCH` | Google Video Search | Tool to perform a google video search via zenserp. use when you need video-specific search results. | | `ZENSERP_YANDEX_SEARCH` | Yandex Search via Zenserp | Tool to obtain yandex search results via zenserp api. use when you need programmatic access to yandex search data after constructing a query. | | `ZENSERP_ZENSERP_GOOGLE_IMAGE_SEARCH` | Zenserp Google Image Search | Tool to perform a google image search via zenserp. use when you need structured image search results for a specific query. | | `ZENSERP_ZENSERP_GOOGLE_MAPS_SEARCH` | Google Maps Search | Tool to perform a google maps (local) search. use when you need localized business results for a given query. provide 'location' or 'lat'/'lng' for geotargeting. | | `ZENSERP_ZENSERP_GOOGLE_SEARCH` | Zenserp Google Search | Tool to perform a standard google search via zenserp. use when you need structured serp data for a given query. | | `ZENSERP_GOOGLE_SHOPPING_SEARCH` | Google Shopping Search | Tool to perform a google shopping search. use when you need structured product offers and pricing data via zenserp api. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Zenserp MCP server is an implementation of the Model Context Protocol that connects your AI agent to Zenserp. It provides structured and secure access so your agent can perform Zenserp operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Zenserp connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies **What's happening:** - composio connects your agent to Zenserp via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Zenserp MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Zenserp **What's happening:** - You create a Zenserp only session through Composio - Composio returns an MCP HTTP URL that exposes Zenserp tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["zenserp"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["zenserp"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Zenserp through Composio's Tool Router. The agent can perform Zenserp operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## How to build Zenserp MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/zenserp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zenserp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zenserp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zenserp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zenserp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zenserp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zenserp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zenserp/framework/cli) - [Google ADK](https://composio.dev/toolkits/zenserp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zenserp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zenserp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zenserp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zenserp/framework/llama-index) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Zenserp MCP? With a standalone Zenserp MCP server, the agents and LLMs can only access a fixed set of Zenserp tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zenserp and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with CrewAI? Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Zenserp tools. ### Can I manage the permissions and scopes for Zenserp while using Tool Router? Yes, absolutely. You can configure which Zenserp scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Zenserp data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)